Preeclampsia, a hypertensive disorder of pregnancy, is a leading cause of maternal and neonatal morbidity and mortality around the world, responsible for approximately 76,000 maternal and 500,000 infant deaths per year. As a heterogeneous and poorly understood disorder, its pathogenesis and possible treatments are an area of active research. While the majority of morbidity and mortality from the disorder can be prevented with close monitoring and treatment in a tertiary care center, atypical presentations with delayed diagnosis and inability to accurately predict which women will progress to a severe form complicates management. Additionally, many women are treated unnecessarily at untold cost in prolonged antepartum admissions, monitoring, and complications from misguided therapy. Technology to accurately diagnose preeclampsia, or better yet predict which women will develop the disease, would enhance treatment and save lives. Current research focuses on an array of biomarkers in the bloodstream; a viable, if expensive, opportunity for both prediction and improved diagnostic accuracy. The current proposal seeks a much less costly alternative. The vascular derangement that underlies preeclampsia develops months before the onset of symptoms. Early work shows these changes are detectable by analyzing features of the maternal cardiovascular system. The current project will develop and test an inexpensive, reusable, non-invasive device that accurately diagnoses preeclampsia. The device employs a single ECG lead and a pulse oximetry probe. Software algorithms extract more than 80 features from these two technologies. The phases of the project include 1) collect data on 100 preeclamptic and 100 control parturients in labor & delivery with known diagnosis, using a bench-top system; 2) refine preliminary algorithms with regression and nonlinear techniques; 3) develop a miniaturized prototype of the device and validate performance. For Phase II the device will be employed in two additional studies: (1) longitudinal data collection in prenatal clinic patients to refine algorithms for prediction of preeclampsia months before onset and (2) large-scale data collection in delivery wards for development of algorithms that distinguish mild preeclamptics from those who will go on to develop severe disease. The eventual goal is a small, inexpensive, reusable, non-invasive device for use in clinics, emergency departments and delivery wards to predict and diagnose preeclampsia and differentiate future mild and severe disease. The cost of the device allows for a per use cost of as little as five cents. It will support better decision making regarding patient management including level of monitoring, delivery planning and therapeutic interventions.